State observer scheme for joint kinetic parameter and state estimation

Hulhoven, Xavier; Wouwer, Alain Vande; Bogaerts, Philippe

doi:10.1016/j.ces.2007.11.042

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…It would be interesting to explore alternative robust estimation schemes, requiring minimal kinetic modeling, but possibly offering smoother recovery from large perturbations. One such scheme could be the hybrid observer proposed in [30], which combines an asymptotic observer and a receding horizon estimator.…”

Section: Discussionmentioning

confidence: 99%

Experimental Validation of a Cascade Control Strategy for Continuously Perfused Animal Cell Cultures

et al. 2020

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This paper is dedicated to the experimental validation of a cascade control strategy for simultaneously regulating the glucose and biomass levels in continuously perfused HEK-293 cell cultures. The inner loop consists of a partial feedback linearization, which requires the estimation of the biomass specific growth rate and glucose uptake rate. This latter task is achieved by sliding mode observers, which do not require a priori process knowledge in the form of a process model. The linearized process is then regulated by the outer loop, including two classical PI controllers with autotuning. The four manipulated variables are a feed flow rate with low glucose concentration, another feed flow with a higher glucose content, a bleed flow, and a perfusion stream. The experimental results demonstrate the ability of the control strategy to reach and regulate the prescribed setpoints. The main advantage of the strategy is that it can be applied in a plug and play manner and shows satisfactory robustness. To the best of our knowledge, this is the first time that such a multivariable control strategy, together with sliding mode observers, is applied at the lab scale to an industrial process in the pharmaceutical sector.

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Section: Discussionmentioning

confidence: 99%

Experimental Validation of a Cascade Control Strategy for Continuously Perfused Animal Cell Cultures

et al. 2020

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“…Although a specific sensor could be available, this kind of hardware is usually expensive. According to this, there are many studies for state estimation in chemical and biochemical processes (Bogaerts and Coutinho, 2014, Bogaerts and Wouwer, 2003, Dewasme et al, 2015, Hulhoven et al, 2006, Lara-Cisneros et al, 2016, Heidarinejad et al, 2012, Kravaris et al, 2007, Hulhoven et al, 2008, Oliveira-Esquerre et al, 2002, Pantano et al, 2017b.…”

Section: Neural State Estimation Designmentioning

confidence: 99%

State Estimation and Trajectory Tracking Control for a Nonlinear and Multivariable Bioethanol Production System

Fernández¹,

Pantano²,

Rossomando³

et al. 2019

Braz. J. Chem. Eng.

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In this paper a controller is proposed based on linear algebra for a fed-batch bioethanol production process. It involves finding feed rate profiles (control actions obtained as a solution of a linear equations system) in order to make the system follow predefined concentration profiles. A neural network states estimation is designed in order to know those variables that cannot be measured. The controller is tuned using a Monte Carlo experiment for which a cost function that penalizes tracking errors is defined. Moreover, several tests (adding parametric uncertainty and perturbations in the control action) are carried out so as to evaluate the controller performance. A comparison with another controller is made. The demonstration of the error convergence, as well as the stability analysis of the neural network, are included.

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“…The goal of the case study was to use a mechanistic bioprocess model for monitoring or predicting process states that are actually unmeasurable during a bioprocess. In these cases, frequently state observers like the Kalman filter are used . Another goal is to use the mechanistic model for controlling the corresponding bioprocess .…”

Section: Examples and Case Studiesmentioning

confidence: 99%

“…However, when applying mechanistic models in a real‐time context, usually reference measurements are needed, e.g., in the case of closed‐loop control, a measurement of the state to be controlled, e.g., a substrate concentration, acts as feedback for the controller . Also, when using an observer to predict the current process state, measurements are needed . Due to observability aspects, measurements of key process variables like biomass, substrate, or metabolites are needed .…”

Section: Examples and Case Studiesmentioning

confidence: 99%

Between the Poles of Data‐Driven and Mechanistic Modeling for Process Operation

Solle

Hitzmann

Herwig

et al. 2017

Chemie Ingenieur Technik

100

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The best method for process control is the use of model‐based solutions, based on process analytical technology for online monitoring of critical process variables, product quality attributes, or a holistic process state estimation. Mechanistic models as well as data‐driven techniques are essential for real‐time process monitoring. Their main characteristics, advantages and disadvantages, and the link between both are discussed as well as the synergetic effects, benefits, and drawbacks resulting from their combination. Aspects and differences of the computational model life cycle management are highlighted.

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State observer scheme for joint kinetic parameter and state estimation

Cited by 10 publications

References 11 publications

Experimental Validation of a Cascade Control Strategy for Continuously Perfused Animal Cell Cultures

Experimental Validation of a Cascade Control Strategy for Continuously Perfused Animal Cell Cultures

State Estimation and Trajectory Tracking Control for a Nonlinear and Multivariable Bioethanol Production System

Between the Poles of Data‐Driven and Mechanistic Modeling for Process Operation

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